Open Access

Molecular Dissection of Seedling Salinity Tolerance in Rice (Oryza sativa L.) Using a High-Density GBS-Based SNP Linkage Map

  • Teresa B. De Leon1,
  • Steven Linscombe2 and
  • Prasanta K. Subudhi1Email author
Rice20169:52

https://doi.org/10.1186/s12284-016-0125-2

Received: 6 May 2016

Accepted: 23 September 2016

Published: 1 October 2016

Abstract

Background

Salinity is one of the many abiotic stresses limiting rice production worldwide. Several studies were conducted to identify quantitative trait loci (QTLs) for traits associated to salinity tolerance. However, due to large confidence interval for the position of QTLs, utility of reported QTLs and the associated markers has been limited in rice breeding programs. The main objective of this study is to construct a high-density rice genetic map for identification QTLs and candidate genes for salinity tolerance at seedling stage.

Results

We evaluated a population of 187 recombinant inbred lines (RILs) developed from a cross between Bengal and Pokkali for nine traits related to salinity tolerance. A total of 9303 SNP markers generated by genotyping-by-sequencing (GBS) were mapped to 2817 recombination points. The genetic map had a total map length of 1650 cM with an average resolution of 0.59 cM between markers. For nine traits, a total of 85 additive QTLs were identified, of which, 16 were large-effect QTLs and the rest were small-effect QTLs. The average interval size of QTL was about 132 kilo base pairs (Kb). Eleven of the 85 additive QTLs validated 14 reported QTLs for shoot potassium concentration, sodium-potassium ratio, salt injury score, plant height, and shoot dry weight. Epistatic QTL mapping identified several pairs of QTLs that significantly contributed to the variation of traits. The QTL for high shoot K+ concentration was mapped near the qSKC1 region. However, candidate genes within the QTL interval were a CC-NBS-LRR protein, three uncharacterized genes, and transposable elements. Additionally, many QTLs flanked small chromosomal intervals containing few candidate genes. Annotation of the genes located within QTL intervals indicated that ion transporters, osmotic regulators, transcription factors, and protein kinases may play essential role in various salt tolerance mechanisms.

Conclusion

The saturation of SNP markers in our linkage map increased the resolution of QTL mapping. Our study offers new insights on salinity tolerance and presents useful candidate genes that will help in marker-assisted gene pyramiding to develop salt tolerant rice varieties.

Keywords

Oryza sativa Genotyping by sequencing Quantitative trait locus Salt tolerance Candidate gene Single nucleotide polymorphism

Background

Rice is a staple food crop for many countries in Asia, Africa, and Latin America. In spite of increased production worldwide, rice growers are faced with challenges caused by both biotic and abiotic stresses. Hence, breeding programs targeted to address those problems are implemented. Among the abiotic stresses, soil and water salinity is a problem not only in the coastal areas but also in areas where crop production heavily relies on irrigation with poor drainage system. Previous studies have indicated that rice is sensitive to salt stress during seedling stage and reproductive stage (Pearson and Bernstein 1959; Zeng et al. 2001). Rice seedlings wither and eventually die at 10dSm-1 salt stress (Munns et al. 2006) while yield loss can be as high as 90 % at 3dSm-1 salt level (Asch et al. 2000). Progress in breeding rice with salt tolerance is slow due to genetic complexity of salinity tolerance (Flowers and Flowers 2005). Some germplasms with high salt tolerance are available. However, majority of these germplasms possess many undesirable traits. Pokkali, Nona Bokra, and Hasawi, which are highly tolerant and often used as donors in breeding for salt tolerance, are tall, photosensitive, low yielding, and have red kernel. In addition, salt tolerance screening is difficult because the phenotypic response of rice to salt stress is highly affected by other confounding environmental factors (Gregorio and Senadhira 1993; Flowers 2004). Hence, the search for QTLs and DNA markers tightly linked to traits related to salt tolerance becomes a major objective in most breeding programs. It is assumed that molecular markers will facilitate a fast and cost-effective screening of large populations (Munns and James 2003).

Since the advent of molecular markers, QTL analyses for salinity tolerance at seedling stage were conducted using RIL (Koyama et al. 2001; Gregorio et al. 2002; Wang et al. 2012), F2:3 lines (Lin et al. 2004), and backcross populations (Thomson et al. 2010; Alam et al. 2011). QTLs for visual scoring, survival, shoot and root lengths, Na+/K+ ratio, Na+ and K+ concentrations, in root and shoot were frequently investigated at 100–120 mM salt stress. Most of the QTL mapping studies have indicated polygenic nature of salinity tolerance. Among the QTLs for traits related to salt tolerance, only qSKC1 was successfully isolated by map-based cloning (Ren et al. 2005). The SKC1 gene from Nona Bokra encodes an HKT-type transporter that regulates the Na+/K+ homeostasis under salt stress. In earlier reports, the QTL designated as Saltol (Gregorio 1997) and a gene ‘SalT’ (Causse et al. 1994) for Na+/K+ ratio were located on chromosome 1.

Numerous QTL mapping studies for salinity tolerance were based on linkage maps constructed using AFLP (Gregorio 1997), RFLP (Koyama et al. 2001; Bonilla et al. 2002; Lin et al. 2004), and SSR markers (Thomson et al. 2010; Wang et al. 2012). The population size was usually small and the markers were sparse due to limited polymorphism between the parents. The rapid development in the sequencing technology makes single nucleotide polymorphism (SNP) to become the marker of choice for QTL mapping. Bimpong et al. (2013) used 194 polymorphic SNP markers for mapping QTLs related to salinity tolerance. More recently, Kumar et al. (2015) applied the genome-wide association (GWAS) mapping on 220 rice varieties using a custom-designed array containing 6000 SNPs. Major association of Na+/K+ ratio still co-localized to the Saltol locus with additional QTLs on chromosome 4, 6, and 7. Significant SNPs were identified and some candidate genes were suggested. However, tight association of candidate genes in or around a single variant still needs enrichment with more markers at a locus to avoid false association. Moreover, complete resequencing of the locus in tolerant and non-tolerant lines or in bi-parental population are needed to add credence to the robustness of GWAS using SNP array.

The introduction of genotyping-by-sequencing (GBS) and the availability of whole genome sequence of rice have accelerated the identification of millions of SNPs across the whole genome. To date, GBS is becoming popular for population studies, genetic diversity, QTL mapping, and genomic selection (He et al. 2014). GBS enabled the construction of high-density linkage map and QTL analysis in maize, wheat, barley (Poland et al. 2012; Chen et al. 2014), oat (Huang et al. 2014), and chickpea (Jaganathan et al. 2015). In rice, GBS has been applied in QTL mapping for leaf width and aluminum tolerance (Spindel et al. 2013), pericarp color and some agronomic traits (Arbelaez et al. 2015), and rice blast resistance (Liu et al. 2015). Several QTL mapping studies for salinity tolerance have been reported. However, QTLs and markers flanking QTLs for salinity tolerance are not being utilized in breeding programs. The main reason for this is attributed to the large chromosome intervals delimited by those QTLs. Thus, identification of candidate genes and understanding of salinity tolerance mechanism still remained a challenge.

In this study, a recombinant inbred line population at F6 generation, developed from the cross Bengal x Pokkali, was used. Bengal is a high yielding, early maturing, semi-dwarf, medium grain cultivar developed from the cross of MARS//M201/MARS (Linscombe et al. 1993). It is sensitive to salinity stress (De Leon et al. 2015). Pokkali is a highly tolerant landrace often used as a donor for salinity tolerance. However, it is notable for many undesirable traits such as low-yield, tall, and highly susceptible to lodging. It is photoperiod-sensitive, awned, with red pericarp and poor cooking quality (Gregorio et al. 2002). We used the GBS technique to construct a high-resolution genome-wide SNP genetic map for identification of additive and epistatic QTLs for salinity tolerance. Segregation distortion loci (SDLs) and QTLs for plant height were mapped to show the quality and accuracy of the genetic map and QTL mapping. Our ultra-high density map allowed us to map QTLs with high resolution and identify candidate genes that may play important role in the mechanism of salt tolerance in rice. The candidate genes identified in this study will serve as useful targets for functional genomics, gene pyramiding, and for gene-based marker-assisted breeding for salinity tolerance.

Results

Phenotypic Characterization Under Salt Stress

The parents and RIL population were evaluated under salt stress for salt injury score (SIS), chlorophyll content (CHL), shoot length (SHL), root length (RTL), shoot length to root length ratio (SRR), dry shoot weight (DWT), shoot Na+ and K+ concentrations, and Na+/K + ratio (NaK ratio). At 12dSm-1 salt stress, the RILs and parents showed varying levels of tolerance. Bengal and Pokkali showed significant contrasting response in SIS, SHL, RTL, DWT, and NaK ratio (Table 1). However, the differences in CHL, SRR, Na+ and K+ concentrations, were not statistically significant between parents. Pokkali showed consistently lower SIS, Na+ concentration, NaK ratio, and higher K+ concentration than Bengal. Among the RILs, all traits showed significant genotypic differences (p <0.0001), indicating a wide range of variation. The RIL population had a mean value between the parental means for all traits except in CHL and SRR. Pokkali had an average SIS of 3; Bengal had 8.4, while the RILs had a mean SIS of 4.7. The RIL population had a mean Na+ accumulation of 1430 mmolkg-1 in shoot, which is much lower than Bengal (1700 mmolkg-1), and marginally higher than Pokkali (1424 mmolkg-1). In contrast, the mean K+ accumulation was highest in Pokkali (591 mmolkg-1), followed by RILs (547 mmolkg-1) and lowest in Bengal (420 mmolkg-1). The RIL population had mean chlorophyll content greater than either parent. As indicated in the frequency distribution (Fig. 1) and the range of RIL values for each trait (Table 1), several lines were phenotypically superior to the parents. There were many transgressive segregants with much lower Na+ than Bengal, lower NaK ratio and SHL and higher CHL, DWT, RTL and SRR than Bengal or Pokkali. Similarly, some lines accumulated twice the K+ concentration of Pokkali. But there was no line that showed higher tolerance than Pokkali as judged by SIS (Fig. 1). There was wide variation for heritability values for traits. Heritabilities for Na+, K+ concentrations, and SHL were 0.98, 0.95, and 90, respectively. In contrast, NaK ratio, SIS, CHL, RTL, and SRR had moderate heritability of 0.24–0.63 while DWT has very low heritability.
Table 1

Phenotypic response of parents and F6 RIL population for traits related to salt tolerance at seedling stage

Trait Name

Bengal Mean

Pokkali Meanβ

RIL Mean

Std. Dev.

RIL Range

RIL Pr > F§

Heritability¥

Na+ (mmolkg-1)

1700.00

1424.3ns

1430.7

246.24

861.97–2733.35

<0.0001

0.98

K+ (mmolkg-1)

420.00

591ns

547.3

107.59

335.99–884.18

<0.0001

0.95

NaK (ratio)

4.07

2.38**

2.8

0.56

1.25–5.32

<0.0001

0.24

SIS

8.40

3.00***

4.7

0.72

3.00–8.73

<0.0001

0.44

CHL (SPAD unit)

20.56

19.54ns

24.2

4.25

13.72–43.67

<0.0001

0.45

SHL (cm)

32.07

44.52***

40.7

3.21

22.60–59.73

<0.0001

0.90

RTL (cm)

6.73

10.08**

7.4

0.64

4.67–11.27

<0.0001

0.61

DWT (g)

0.06

0.11*

0.1

0.01

0.04–0.16

<0.0001

0.01

SRR (ratio)

4.98

4.53ns

5.6

0.53

3.08–9.79

<0.0001

0.63

Na+: shoot sodium concentration, K+: shoot potassium concentration, NaK: ratio of the shoot sodium and shoot potassium content, SIS: salt injury score, CHL: chlorophyll content, SHL: shoot length, RTL: root length, DWT: shoot dry weight, SRR: shoot length to root length ratio

βSignificant differences between Bengal and Pokkali, nsno significant differences, *significant at 0.05 probability level, **significant at 0.01 probability level, ***significant at 0.001 probability level

§Genotypic differences among RIL

¥Broad sense heritability computed on family mean basis

Fig. 1

Frequency distribution of Bengal/Pokkali F6 RIL population for traits related to seedling salinity tolerance. Na+ Conc., Na+ concentration; K+ conc., K+ concentration; NaK, Na+/K+ ratio; SIS, log transformed salt injury score; CHL, chlorophyll content measured by SPAD-502 unit; DWT, dry weight; SHL, shoot length; RTL, root length; SRR, Shoot length to root length ratio

Correlation of Traits

Correlations among all traits (Table 2) revealed that SIS was highly significant and positively correlated to Na+ concentration and NaK ratio. The SIS was highly significant and negatively correlated to CHL, SHL, RTL, DWT and SRR, indicating the negative effect of salt stress on the overall growth and photosynthetic capability of plants. On the other hand, K+ concentration was positively correlated to Na+ concentration, SHL, CHL, DWT, and SRR but negatively correlated to NaK ratio, SIS, and RTL. The relationships between traits in RIL population were consistent to the correlation of traits observed among the 30 US rice genotypes (De Leon et al. 2015), thus indicating reliability and reproducibility of our salt tolerance screening.
Table 2

Pearson correlation matrix of traits measured in response to salt stress at 12dSm-1 in Bengal/Pokkali F6 RIL population at seedling stage

 

Na+

K+

NaK

SIS

CHL

SHL

RTL

DWT

SRR

Na+

1

        

K+

0.1271**

1

       

NaK

0.594***

-0.649***

1

      

SIS

0.337***

-0.129**

0.337***

1

     

CHL

-0.128**

0.092*

-0.157***

-0.214***

1

    

SHL

0.039

0.253***

-0.151***

-0.236***

0.221***

1

   

RTL

0.057

-0.105*

0.095*

-0.109**

0.059

0.204***

1

  

DWT

0.006

0.144***

-0.099*

-0.475***

0.177***

0.539***

0.279***

1

 

SRR

-0.024

0.277***

-0.195***

-0.099*

0.111**

0.593***

-0.638***

0.173*

1

Na+: shoot sodium concentration, K+: shoot potassium concentration, NaK: ratio of the shoot sodium and shoot potassium content, SIS: salt injury score, CHL: chlorophyll content, SHL: shoot length, RTL: root length, DWT: dry weight, SRR: shoot length to root length ratio

*significant at 0.05 probability level, **significant at 0.01 probability level, ***significant at 0.001 probability level

Linkage Mapping

GBS generated a total of 33,987 SNP markers which were furtherly filtered for polymorphic markers and for markers with less than 10 % missing data across the population. A total of 9303 SNPs markers were retained and used in the linkage map construction (Fig. 2, Additional file 1: Table S1). On the average, about 775 SNP markers were placed per chromosome (Table 3). The final linkage map had a total length of 1650 cM with 2817 recombination sites. The average distance between adjacent markers was 0.59 cM or 39,798 bp, with maximum resolution of 0.27 cM. The average marker density was 5.6 SNP markers per cM or 3.3 SNP markers per recombination point. The map was saturated with SNP markers across all chromosomes. However, twenty large gaps were observed on chromosomes 1, 2, 3, 4, 6, 7, 8, 10, 11, and 12 that ranged between 5 cM to 13 cM. With 9303 SNP markers, the linkage map had a physical to genetic map length ratio of 225 Kb/cM.
Fig. 2

Molecular genetic map showing the positions of QTLs for nine traits investigated under salt stress. Linkage and QTL mapping were implemented in ICIM QTL Mapping 4.0 using 9303 GBS-SNP markers in 187 Bengal/Pokkali F6 RILs. Chromosome regions that are dark indicate the saturation of markers while regions that are white indicate the absence of marker placed in those segments. Genetic distance in centimorgan was determined by Kosambi map function. Each arrow represents a single QTL for a particular trait

Table 3

Summary distribution, coverage, and intervals of SNP markers in the Bengal/Pokkali RIL linkage map

Chromosome

No. of SNP markers used

Chromosome length coverage (Mb)

Genetic length (cM)

No. of recombination points

No. of SNP markers/cM

No. of SNP markers/unique position

Minimum interval (cM)

Maximum interval (cM)

Average Interval

(cM)

No. of Gaps

>5 cM

1

1245

43,237,333

199.8

363

6.2

3.4

0.27

9.98

0.55

2

2

1001

35,875,736

182.9

324

5.5

3.1

0.27

7.19

0.56

2

3

1068

36,405,799

191.2

320

5.6

3.3

0.28

8.01

0.60

2

4

822

35,501,387

148.1

244

5.6

3.4

0.27

6.88

0.60

2

5

780

29,507,277

135.6

243

5.8

3.2

0.27

4.35

0.56

0

6

842

30,869,147

148.6

258

5.7

3.3

0.27

5.33

0.57

1

7

736

29,582,943

127.4

225

5.8

3.3

0.27

8.7

0.57

1

8

471

28,399,689

113.9

162

4.1

2.9

0.27

10.57

0.70

3

9

584

22,779,506

85.9

164

6.8

3.6

0.27

6.49

0.52

1

10

517

23,117,196

95.2

149

5.4

3.5

0.28

11.55

0.64

1

11

622

28,973,227

121.8

187

5.1

3.3

0.28

13.09

0.65

3

12

615

27,488,377

99.9

178

6.2

3.5

0.27

6.75

0.56

2

Total

9303

371,737,617

1650.2

2817

67.7

39.7

3.27

98.89

7.08

20

Averagea

775.3

30,978,134.75

137.5

234.8

5.6

3.3

0.27

8.24

0.59

1.7

aAverage value per chromosome

Identification of Additive and Di-genic Epistatic QTLs for Traits Related to Salinity Tolerance

To detect novel additive and epistatic QTLs for traits related to salinity tolerance, the phenotype and GBS data were used in interval mapping (IM) and inclusive composite interval mapping (ICIM) methods.

QTLs for Shoot Na+ Concentration

The IM and ICIM methods consistently detected three additive QTLs for shoot Na+ concentration (Table 4). The QTLs were located on chromosomes 2, 6, and 12. Each additive QTL explained at least 5.5 % of the phenotypic variation. Pokkali alleles of qNa2.7 and qNa12.18 had increasing effect while for qNa6.5 Bengal allele had the increasing effect. Interval mapping of epistatic QTLs detected seven pairs of QTLs with significant contribution to the variation in Na+ concentration (Table 5). Four of the seven pairs of epistatic QTLs had large effects (PVE = 11–16 %) while the other three pairs had small effects (PVE = 8–9 %). Nine interacting QTLs with increasing effect were from Bengal and five were from Pokkali. None of the additive QTLs co-localized with epistatic QTLs.
Table 4

Additive QTLs for traits related to seedling-stage salt tolerance in Bengal/Pokkali F6 RIL population identified by IM and ICIM methods

Phenotype

QTL

Chra

Position (cM)

Left Marker

Right Marker

QTL Interval Size (bp)

LOD

PVE (%)

Additive Effect

Parental Source of Increasing Allele Effectb

No. of genes in QTL interval

Na+ concentration-IM

qNa2.7

2

48

S2_7769844

S2_7939496

169,652

2.2969

5.55

-66.59

P

24

 

qNa6.5

6

34

S6_5269698

S6_5533752

264,054

2.399

5.97

69.15

B

34

 

qNa12.18

12

60

S12_18687038

S12_18741493

54,455

2.2496

5.51

-66.36

P

5

Na+ concentration-ICIM

qNa2.7

2

48

S2_7769844

S2_7939496

169,652

2.2969

5.55

-66.59

P

24

 

qNa6.5

6

34

S6_5269698

S6_5533752

264,054

2.399

5.97

69.15

B

34

 

qNa12.18

12

60

S12_18687038

S12_18741493

54,455

2.2496

5.51

-66.36

P

5

K+ concentration-IM

qK1.8

1

63

S1_8656025

S1_8901503

245,478

5.7933

13.65

-46.34

P

33

 

qK1.11

1

71

S1_11529325

S1_11581799

52,474

5.9263

13.66

-45.13

P

6

 

qK1.38

1

173

S1_38794029

S1_39047133

253,104

3.5096

8.30

-33.32

P

40

 

qK5.4

5

31

S5_4699921

S5_5326365

626,444

2.2496

5.51

-27.00

P

86

 

qK6.4

6

31

S6_4890290

S6_5269698

379,408

3.333

8.21

-32.92

P

61

K+ concentration-ICIM

qK1.11

1

71

S1_11529325

S1_11581799

52,474

7.7414

16.08

-48.95

P

6

 

qK1.38

1

173

S1_38794029

S1_39047133

253,104

5.3793

10.71

-37.86

P

40

NaK ratio-IM

qNaK1.11

1

71

S1_11529325

S1_11581799

52,474

4.154

9.83

0.29

B

6

 

qNaK6.2

6

15

S6_2927160

S6_2962502

35,342

3.5777

8.46

0.26

B

7

 

qNaK6.5

6

33

S6_5269698

S6_5533752

264,054

5.1164

13.21

0.32

B

34

NaK ratio-ICIM

qNaK1.11

1

71

S1_11529325

S1_11581799

52,474

2.6375

5.66

0.22

B

6

 

qNaK6.5

6

33

S6_5269698

S6_5533752

264,054

3.7097

8.85

0.26

B

34

Salt injury score-IM

qSIS2.8

2

50

S2_8730258

S2_8927908

197,650

3.5375

8.58

-0.06

P

25

 

qSIS2.19

2

81

S2_19331684

S2_19454952

123,268

3.2133

7.66

-0.06

P

14

 

qSIS2.28

2

131

S2_28239596

S2_28274467

34,871

2.6449

6.37

-0.05

P

8

 

qSIS5.03

5

1

S5_312457

S5_329699

17,242

2.8257

6.74

0.06

B

4

 

qSIS5.1a

5

12

S5_1686924

S5_1707475

20,551

2.8266

6.76

0.06

B

5

 

qSIS5.24

5

106

S5_24057323

S5_24281632

224,309

3.1266

7.51

0.06

B

39

 

qSIS6.2

6

15

S6_2927160

S6_2962502

35,342

2.0824

5.04

0.05

B

7

 

qSIS6.5

6

37

S6_5848568

S6_5905669

57,101

3.0401

7.23

0.06

B

11

 

qSIS6.7

6

48

S6_7646442

S6_7661883

15,441

3.1238

7.41

0.06

B

3

 

qSIS6.20

6

90

S6_20929261

S6_20929283

22

3.9605

9.44

0.07

B

1

 

qSIS11.2

11

18

S11_2838776

S11_3716306

877,530

2.664

8.36

0.06

B

136

Salt injury score-ICIM

qSIS5.1b

5

11

S5_1441967

S5_1454837

12,870

9.7068

13.33

0.08

B

2

 

qSIS6.2b

6

9

S6_2123411

S6_2242943

119,532

3.5933

4.46

0.05

B

23

 

qSIS6.21

6

92

S6_21253244

S6_21256132

2888

6.9204

9.11

0.07

B

1

 

qSIS7.14

7

57

S7_14598897

S7_14625841

26,944

3.6209

4.50

0.05

B

7

 

qSIS8.24

8

93

S8_24763939

S8_25110888

346,949

2.6235

3.28

0.04

B

47

 

qSIS9.8

9

13

S9_8608506

S9_9070610

462,104

7.09

9.19

0.07

B

51

 

qSIS11.2

11

21

S11_2838776

S11_3716306

877,530

2.336

3.53

0.04

B

136

Chlorophyll content-IM

qCHL11.1

11

5

S11_1086712

S11_1293020

206,308

2.1922

5.41

-1.00

P

34

 

qCHL11.2

11

14

S11_2666525

S11_2724222

57,697

2.0172

4.86

-0.95

P

7

Chlorophyll content-ICIM

qCHL2.20

2

86

S2_20258450

S2_20346560

88,110

3.6938

7.44

1.18

B

7

 

qCHL2.30

2

143

S2_30353435

S2_30402468

49,033

2.3418

4.69

-0.94

P

7

 

qCHL3.26

3

136

S3_26705619

S3_26709038

3419

3.2263

6.42

-1.10

P

1

Shoot length-IM

qSHL1.1

1

11

S1_1708228

S1_1747144

38,916

2.0367

5.03

-1.42

P

7

 

qSHL1.7a

1

48

S1_7259818

S1_7296346

36,528

3.9307

9.26

-1.95

P

7

 

qSHL1.38

1

168

S1_38286772

S1_38611845

325,073

25.3529

48.03

-4.43

P

52

 

qSHL3.34

3

185

S3_34720589

S3_35060080

339,491

2.361

5.65

1.54

B

69

 

qSHL5.4

5

29

S5_4565557

S5_4699921

134,364

2.3239

5.64

-1.52

P

23

 

qSHL5.6

5

44

S5_6356744

S5_6433933

77,189

2.0324

4.96

-1.41

P

13

Shoot length-ICIM

qSHL1.7b

1

50

S1_7520182

S1_7569628

49,446

6.2678

5.86

-1.57

P

5

 

qSHL1.38

1

168

S1_38286772

S1_38611845

325,073

36.9075

51.64

-4.59

P

52

 

qSHL2.18

2

77

S2_18806154

S2_18937362

131,208

3.0049

2.71

1.04

B

25

 

qSHL3.34

3

185

S3_34720589

S3_35060080

339,491

4.3994

3.96

1.29

B

69

 

qSHL5.3

5

25

S5_3353753

S5_3506138

152,385

7.0797

6.79

-1.66

P

21

 

qSHL12.25

12

93

S12_25709174

S12_25887173

177,999

2.246

2.05

0.91

B

30

Root length-IM

qRTL1.26

1

121

S1_26421289

S1_26447134

25,845

2.7346

6.52

0.32

B

6

 

qRTL2.24

2

114

S2_24961302

S2_24961342

40

4.1447

9.72

0.39

B

0

 

qRTL2.26

2

120

S2_26028043

S2_26070191

42,148

4.2053

9.91

0.39

B

9

 

qRTL2.33

2

160

S2_33573567

S2_33614297

40,730

3.9359

9.50

0.39

B

7

 

qRTL3.6

3

36

S3_6011601

S3_6027452

15,851

3.4683

8.23

0.36

B

2

 

qRTL3.7

3

44

S3_7130220

S3_7209963

79,743

4.4685

10.70

0.41

B

15

 

qRTL3.10

3

57

S3_10116591

S3_10132745

16,154

5.0358

11.99

0.43

B

2

 

qRTL4.10

4

24

S4_10625625

S4_10726368

100,743

2.0131

4.88

0.28

B

14

 

qRTL8.4

8

37

S8_4558562

S8_4858127

299,565

2.121

5.34

0.36

B

41

 

qRTL8.19

8

59

S8_19884635

S8_19898432

13,797

3.272

7.75

0.41

B

2

 

qRTL8.27

8

109

S8_27238050

S8_27304101

66,051

2.1036

5.13

-0.28

P

9

 

qRTL9.14

9

39

S9_14960521

S9_14976723

16,202

2.6572

6.45

-0.36

P

3

Root length-ICIM

qRTL1.22

1

102

S1_22666852

S1_22677418

10,566

2.2657

3.54

0.23

B

2

 

qRTL1.26

1

121

S1_26421289

S1_26447134

25,845

2.1764

3.41

0.23

B

6

 

qRTL3.9

3

56

S3_9853159

S3_9891061

37,902

4.2907

7.59

0.34

B

7

Dry weight-IM

qDWT1.21

1

97

S1_21707357

S1_21733437

26,080

2.3413

5.60

-0.01

P

6

 

qDWT4.32

4

126

S4_32367131

S4_32367159

28

2.3915

5.73

-0.01

P

1

 

qDWT5.2

5

15

S5_2116055

S5_2167880

51,825

4.6334

10.80

-0.01

P

4

 

qDWT5.4

5

29

S5_4565557

S5_4699921

134,364

6.5783

15.04

-0.01

P

23

 

qDWT5.5

5

42

S5_5997340

S5_6196044

198,704

6.5368

15.47

-0.01

P

32

 

qDWT6.13

6

72

S6_13046472

S6_13097774

51,302

2.0119

5.16

0.00

P

10

 

qDWT6.20

6

90

S6_20929261

S6_20929283

22

3.7538

8.95

-0.01

P

1

 

qDWT6.23

6

102

S6_23812023

S6_24039384

227,361

3.7054

8.91

-0.01

P

32

 

qDWT11.2

11

10

S11_2379158

S11_2402109

22,951

2.4389

6.03

-0.01

P

3

Dry weight-ICIM

qDWT1.40

1

185

S1_40372283

S1_40412316

40,033

2.0722

3.13

0.00

P

6

 

qDWT4.32

4

126

S4_32367131

S4_32367159

28

3.6566

5.93

-0.01

P

1

 

qDWT5.4

5

29

S5_4565557

S5_4699921

134,364

7.5727

12.98

-0.01

P

23

 

qDWT6.06

6

3

S6_692773

S6_782975

90,202

3.7128

6.02

-0.01

P

13

 

qDWT6.24

6

104

S6_24107596

S6_24228831

121,235

4.4604

7.46

-0.01

P

19

Shoot-root ratio-IM

qSRR1.7

1

50

S1_7520182

S1_7569628

49,446

3.7944

9.09

-0.38

P

5

 

qSRR1.29

1

135

S1_29561423

S1_29568978

7555

3.1151

7.42

-0.33

P

2

 

qSRR1.36

1

159

S1_36158467

S1_36189206

30,739

5.8447

13.42

-0.45

P

5

 

qSRR1.382

1

170

S1_38286772

S1_38611845

325,073

10.3107

23.01

-0.59

P

52

 

qSRR2.28

2

133

S2_28317911

S2_28375704

57,793

4.7052

10.96

-0.41

P

7

 

qSRR2.31

2

146

S2_31037977

S2_31043939

5962

3.2045

7.62

-0.34

P

1

 

qSRR2.33

2

160

S2_33573567

S2_33614297

40,730

4.1813

9.90

-0.39

P

7

 

qSRR2.34

2

168

S2_34660774

S2_35085922

425,148

2.9367

7.37

-0.33

P

68

 

qSRR3.8

3

49

S3_8327882

S3_8353264

25,382

2.6453

6.32

-0.31

P

6

 

qSRR3.10

3

57

S3_10116591

S3_10132745

16,154

2.6902

6.58

-0.31

P

2

 

qSRR3.11

3

70

S3_11848358

S3_11865689

17,331

2.4751

5.93

-0.30

P

1

 

qSRR4.10

4

24

S4_10625625

S4_10726368

100,743

2.438

5.91

-0.30

P

14

 

qSRR8.19

8

59

S8_19884635

S8_19898432

13,797

2.3793

5.70

-0.35

P

2

Shoot root ratio-ICIM

qSRR1.7

1

50

S1_7520182

S1_7569628

49,446

6.9282

8.73

-0.37

P

5

 

qSRR1.386

1

171

S1_38636497

S1_38768787

132,290

15.6449

22.43

-0.59

P

22

 

qSRR2.33

2

160

S2_33573567

S2_33614297

40,730

8.5304

10.92

-0.41

P

7

 

qSRR3.9

3

56

S3_9853159

S3_9891061

37,902

4.3278

5.25

-0.28

P

7

 

qSRR8.26

8

107

S8_26716230

S8_26744324

28,094

2.533

3.01

0.21

B

5

a Chromosome where the QTL was located. bParental source of increasing allele effect was either Pokkali (P) or Bengal (B)

Table 5

Di-genic epistatic QTLs for traits related to salt tolerance at seedling stage in Bengal/Pokkali F6 RIL population identified by interval mapping

Phenotype

QTL1

Chr.1

Position1

LeftMarker1

RightMarker1

QTL2

Chr.2

Position2 (cM)

LeftMarker2

RightMarker2

LOD

PVE(%)

Add1

Add2

Add x Add

Na+ concentration

qNa4.25

4

90

S4_25549517

S4_26622324

qNa4.29

4

110

S4_29966056

S4_29968457

3.08

11.02

57.92

-21.10

-102.91

 

qNa3.26

3

135

S3_26536286

S3_26542118

qNa5.008

5

0

S5_87749

S5_96410

3.24

7.90

-13.88

-0.72

78.59

 

qNa1.12

1

75

S1_12583448

S1_12685974

qNa6.2

6

10

S6_2266152

S6_2272501

3.34

9.09

43.52

-14.46

90.61

 

qNa6.17

6

80

S6_17631626

S6_17780076

qNa6.19

6

85

S6_19446057

S6_19585327

3.10

15.64

106.90

-89.69

-208.67

 

qNa6.4a

6

25

S6_4631489

S6_4771954

qNa10.21

10

85

S10_21364298

S10_21407693

3.83

13.99

86.22

46.66

88.20

 

qNa6.4b

6

30

S6_4890290

S6_5269698

qNa11.1

11

5

S11_1086712

S11_1293020

3.26

13.91

58.04

39.35

80.06

 

qNa3.2

3

15

S3_2171559

S3_2250307

qNa11.23

11

100

S11_23611942

S11_23708208

3.16

8.69

5.27

10.52

82.87

K+ concentration

qK1.7

1

60

S1_7778029

S1_8656025

qK2.3

2

15

S2_3207423

S2_3207477

3.50

21.42

-44.62

-22.75

36.16

 

qK2.25

2

115

S2_25166702

S2_25192275

qK3.22

3

115

S3_22976923

S3_23020366

3.04

8.02

5.08

-5.45

31.47

 

qK1.40

1

190

S1_40584495

S1_40894634

qK7.19

7

70

S7_19334046

S7_19406235

3.14

9.07

-12.92

1.97

-32.60

 

qK1.7

1

50

S1_7520182

S1_7569628

qK12.17

12

55

S12_17065005

S12_17195754

3.23

9.03

-13.81

8.04

-32.71

 

qK11.19

11

70

S11_19222100

S11_19245359

qK12.18

12

60

S12_18687038

S12_18741493

3.63

10.31

16.55

-3.15

-34.00

NaK ratio

qNaK1.42

1

195

S1_42138516

S1_42310908

qNaK3.21

3

110

S3_21445493

S3_21628785

3.08

8.71

0.10

0.02

-0.24

 

qNaK6.30

6

145

S6_30296317

S6_30370989

qNaK8.2

8

20

S8_2341829

S8_2949528

3.09

8.68

0.07

-0.13

0.26

 

qNaK6.4a

6

25

S6_4631489

S6_4771954

qNaK10.213

10

85

S10_21364298

S10_21407693

3.58

17.65

0.34

0.12

0.26

 

qNaK7.22

7

90

S7_22936622

S7_22936634

qNaK10.217

10

90

S10_21749293

S10_21786307

3.47

8.73

-0.03

0.03

-0.26

 

qNaK5.16

5

65

S5_16290294

S5_16307102

qNaK11.2

11

15

S11_2838776

S11_3716306

3.55

10.80

-0.05

0.09

-0.26

 

qNaK3.2

3

20

S3_2776106

S3_2780171

qNaK11.24

11

105

S11_24319577

S11_24335733

3.46

9.89

0.00

-0.12

0.26

 

qNaK1.5

1

35

S1_5501756

S1_5792183

qNaK12.19

12

65

S12_19926993

S12_20016304

3.01

8.66

0.06

-0.08

0.24

Salt injury score

qSIS6.2a

6

15

S6_2927160

S6_2962502

qSIS6.30

6

145

S6_30296317

S6_30370989

3.82

15.04

0.04

0.04

0.07

 

qSIS5.18

5

80

S5_18942631

S5_18997491

qSIS9.9

9

15

S9_9351804

S9_9857266

3.16

12.06

0.05

0.05

0.06

 

qSIS3.10

3

65

S3_10992290

S3_11053944

qSIS10.2

10

5

S10_2799960

S10_2837737

4.11

11.59

0.01

0.04

0.07

 

qSIS2.20

2

85

S2_20153436

S2_20182321

qSIS10.11

10

25

S10_11045261

S10_11244588

3.07

14.67

-0.07

0.02

-0.06

 

qSIS3.11

3

70

S3_11848358

S3_11865689

qSIS12.2

12

15

S12_2315570

S12_2397199

3.33

7.96

0.00

0.00

0.06

Chlorophyll

qCHL1.20

1

90

S1_20242882

S1_21276489

qCHL1.21

1

95

S1_21276489

S1_21352851

7.27

29.81

-4.09

4.17

-5.07

content

qCHL3.17

3

105

S3_17083355

S3_17143997

qCHL3.21

3

110

S3_21445493

S3_21628785

4.59

28.05

4.16

-4.42

-4.73

 

qCHL3.21

3

110

S3_21445493

S3_21628785

qCHL7.7

7

50

S7_7781645

S7_7839200

3.31

8.45

-0.29

-0.20

-1.23

 

qCHL8.23

8

90

S8_23657286

S8_24738259

qCHL8.24

8

95

S8_24763939

S8_25110888

5.38

35.72

-4.73

5.06

-3.76

 

qCHL9.12

9

25

S9_12217170

S9_12366675

qCHL9.12

9

30

S9_12915373

S9_14359383

3.89

34.51

-4.77

4.51

-4.09

 

qCHL2.5

2

40

S2_5800279

S2_5848583

qCHL9.18

9

60

S9_18667894

S9_18669560

3.04

7.74

-0.17

-0.44

-1.20

 

qCHL10.18

10

65

S10_18819950

S10_19941928

qCHL10.18

10

70

S10_18819950

S10_19941928

5.68

34.16

-4.44

4.32

-4.33

 

qCHL7.27

7

110

S7_27772814

S7_27803479

qCHL10.21

10

90

S10_21749293

S10_21786307

3.52

13.31

1.14

0.91

1.28

 

qCHL11.4

11

35

S11_4854309

S11_4863888

qCHL11.6

11

40

S11_6970703

S11_7012013

3.97

11.76

-1.18

1.25

-2.15

 

qCHL3.4

3

25

S3_4116916

S3_4311471

qCHL11.24

11

105

S11_24319577

S11_24335733

3.68

11.82

0.28

-0.53

-1.30

Shoot length

qSHL2.1

2

10

S2_1653448

S2_2064517

qSHL2.5

2

40

S2_5800279

S2_5848583

3.84

9.77

-0.12

-0.48

-2.02

 

qSHL4.25

4

95

S4_25549517

S4_26622324

qSHL5.008

5

0

S5_87749

S5_96410

4.32

10.89

-0.16

-0.22

-2.07

 

qSHL4.27

4

100

S4_27678052

S4_27715999

qSHL8.1

8

15

S8_1995144

S8_2005542

3.81

10.15

-0.23

0.09

1.97

 

qSHL2.32

2

155

S2_32339457

S2_32429009

qSHL9.12

9

25

S9_12217170

S9_12366675

3.67

11.31

0.82

-1.54

2.23

 

qSHL1.28

1

130

S1_28157998

S1_28247178

qSHL9.19

9

65

S9_19628929

S9_19696641

3.09

11.19

-0.72

-0.54

1.75

 

qSHL2.34

2

165

S2_34519074

S2_34545438

qSHL10.20

10

80

S10_20682624

S10_20733813

3.34

9.36

-0.15

0.46

1.83

 

qSHL4.32

4

130

S4_32867449

S4_33074444

qSHL10.21

10

90

S10_21749293

S10_21786307

3.47

10.80

-1.40

-0.05

-1.86

Root length

qRTL1.32

1

145

S1_32327040

S1_32418346

qRTL3.10

3

65

S3_10992290

S3_11053944

4.80

17.47

-0.03

0.30

0.41

 

qRTL4.16

4

35

S4_16669714

S4_16706375

qRTL6.25

6

115

S6_25296416

S6_25363541

3.79

12.10

0.19

-0.12

0.37

 

qRTL3.28

3

145

S3_28513488

S3_29240341

qRTL8.23

8

90

S8_23657286

S8_24738259

3.26

9.34

-0.11

-0.05

-0.38

 

qRTL6.15

6

75

S6_15734275

S6_15881397

qRTL9.16

9

50

S9_16775205

S9_16882286

3.02

11.22

0.15

-0.29

0.36

 

qRTL4.33

4

135

S4_33557881

S4_33861248

qRTL10.19

10

75

S10_19941928

S10_20082337

4.15

10.32

-0.01

0.02

-0.41

Dry weight

qDWT3.17

3

105

S3_17083355

S3_17143997

qDWT6.7

6

50

S6_7662391

S6_7749349

3.20

10.64

0.00

0.00

-0.01

 

qDWT6.4

6

30

S6_4890290

S6_5269698

qDWT6.30

6

145

S6_30296317

S6_30370989

3.06

12.61

0.00

-0.01

-0.01

 

qDWT7.1

7

5

S7_1021298

S7_1051320

qDWT7.27

7

110

S7_27772814

S7_27803479

3.43

10.08

0.00

0.00

0.01

 

qDWT5.2

5

20

S5_2483311

S5_2495045

qDWT10.16

10

50

S10_16848745

S10_16898283

3.30

16.39

-0.01

0.00

-0.01

 

qDWT4.16

4

35

S4_16669714

S4_16706375

qDWT10.19

10

75

S10_19941928

S10_20082337

3.34

9.61

0.00

0.00

0.01

 

qDWT3.5

3

35

S3_5859095

S3_5904925

qDWT12.09

12

10

S12_977852

S12_1386213

3.80

12.95

0.00

0.00

-0.01

 

qSRR2.1

2

5

S2_1103758

S2_1653448

qSRR4.27

4

100

S4_27678052

S4_27715999

3.47

11.08

0.00

-0.19

0.35

Shoot-root ratio

qSRR5.2

5

15

S5_2116055

S5_2167880

qSRR5.5

5

40

S5_5798670

S5_5909747

3.85

9.93

0.12

-0.07

-0.39

 

qSRR2.3

2

25

S2_3978527

S2_4234638

qSRR9.14

9

40

S9_14976723

S9_15092089

3.20

10.65

-0.14

0.24

-0.39

 

qSRR1.20

1

90

S1_20242882

S1_21276489

qSRR9.21

9

75

S9_21030508

S9_21083576

3.09

10.97

-0.11

-0.13

0.37

 

qSRR4.18

4

45

S4_18779374

S4_18826971

qSRR10.001

10

0

S10_103050

S10_160013

3.18

9.97

-0.13

-0.16

0.34

QTLs for Shoot K+ Concentration

The IM method detected five additive QTLs (qK1.8, qK1.11, qK1.38, qK5.4, and qK6.4) for shoot K+ concentration. The qK1.8 and qK1.11 were large-effect QTLs, each accounting for at least 13 % of the variation for shoot K+. The other three QTLs had small effects (5–8 % PVE) and were located on chromosomes 1, 5, and 6. The qK1.11 and qK1.38 were also detected by ICIM with LOD values of 7.7 and 5.4, respectively. Both qK1.11 and qK1.38 were large-effect QTLs in ICIM method with PVE of 16 and 10 %. In contrast, qK1.8, qK5.4, and qK6.4 were not detected in ICIM. All additive QTLs for K+ concentration had increasing effects that originated from Pokkali, indicating the importance of Pokkali alleles for increased uptake of K+ in the leaves. Five pairs of epistatic QTLs were detected for K+ concentration. The qK1.7 and the qK2.3 pair had a PVE of 21 % and LOD score of 3.5, with Pokkali allele contributing for increased K+ accumulation. The qK1.7 also interacted with qK12.17 and accounted for 9 % of the variation in K+ accumulation. Additionally, qK11.19 and qK12.18 pair had a PVE of 10 % while the remaining two pairs accounted for 9 % of the phenotypic variation. Six and four interacting QTLs with increasing effects involved Pokkali and Bengal alleles, respectively. All additive QTL positions were independent of epistatic QTLs.

QTLs for NaK Ratio

For NaK ratio, three additive QTLs (qNaK1.11, qNaK6.2, qNaK6.5) were significant in IM method but only two of the additive QTLs (qNaK1.11, qNaK6.5) were detected in ICIM. The qNaK 6.5 explained 13 % of the phenotypic variation while qNaK6.2 and qNaK1.11 were small-effect QTLs. All NaK ratio QTLs had increasing effects due to Bengal alleles. Of the seven pairs of epistatic QTLs, two pairs were large-effect QTLs (PVE = 11 and 18 %) and five pairs were minor QTLs with PVEs lower than 9 %. There was no epistatic QTL found in the same chromosome intervals of additive QTLs for NaK ratio, K+, or Na+ concentrations. Most of the QTL alleles with increasing effects were from Bengal. But four epistatic QTLs with increasing effects were from Pokkali.

QTLs for SIS

A total of 11 chromosomal regions with significant additive effect were detected on chromosomes 2, 5, 6, and 11 by IM. All QTLs are having small effects of at least 5 % but not more than 9 % of the phenotypic variation. Three QTLs were mapped on chromosome 2 (qSIS2.8, qSIS2.29, and qSIS2.28) with increasing effects from Pokkali alleles. In contrast, ICIM detected seven QTLs. The additive QTLs were distributed on chromosomes 5, 6, 7, 8, 9, and 11. The qSIS5.1b was a major QTL, explaining about 13 % of the phenotypic variation. However, qSIS5.1b had increasing salt sensitivity effect from Bengal allele. Except for QTLs on chromosome 2, all other additive QTLs had increasing effects due to Bengal alleles. Between the two mapping methods, all QTLs were different except for qSIS11.2. For epistatic QTLs, five pairs of interacting QTLs were significant of which four pairs explained 11–15 % of the SIS variation. Among the additive QTLs, qSIS6.2 was significantly interacting with qSIS6.30 and increased the PVE from 5 to 15 % (Table 5). All interacting QTLs had increasing effects from Bengal alleles except the qSIS2.20.

QTLs for Chlorophyll Content

A total of five chromosome regions with additive effects were detected for chlorophyll content under salt stress. Two QTLs were detected on chromosome 11 by IM while ICIM detected two QTLs on chromosome 2 and one QTL on chromosome 3. All additive QTLs were minor-effect QTLs, with increasing CHL effects from Pokkali alleles except qCHL2.20. In contrast, epistatic QTL mapping detected ten significant pairs of interacting QTLs. Eight QTL pairs had large effects with PVE as high as 36 %. All additive QTLs were independent of epistatic QTLs for CHL.

QTLs for Shoot Length

Six additive QTLs were detected by IM and another six QTLs were detected by ICIM. The qSHL1.38 and qSHL3.34 were significant QTLs in both methods. The qSHL1.38 was a major QTL with LOD value of 37 and accounted for 48–52 % of the phenotypic variation. The additive effect of qSHL1.38 had increasing effect from Pokkali allele. Other SHL QTLs were located on chromosome 2, 3, 5, and 12 with small effects. Seven pairs of QTLs were significant in epistatic QTL mapping. Five pairs had 11 % PVE and the other two pairs had 9 % PVE. There was no epistatic QTL that co-localized with additive QTL.

QTLs for Root Length

Twelve additive QTLs were detected for root length by IM. In contrast, ICIM detected only three QTLs, with qRTL1.26 common in both methods. Two large-effect QTLs in chromosome 3 (qRTL3.7 and qRTL3.10) were highly significant and accounted for 10 and 12 % of the phenotypic variation, respectively. Both QTLs had increasing effects from Bengal alleles. All other QTLs were minor-effect QTLs, with increasing effects contributed by Bengal allele. Five significant pairs of interacting QTLs with PVE ranging between 9 and 17 % were detected. None of the interacting QTLs were found similar or co-localizing to additive QTLs.

QTLs for dry Weight

For shoot dry weight, nine additive QTLs were significant by IM. Three QTLs located on chromosome 5 (qDWT5.2, qDWT5.4 and qDWT5.5) were large-effect QTLs that accounted for 11, 15 and 15 % of the phenotypic variation, respectively. Other QTLs were distributed on chromosomes 1, 4, 6, and 11, with PVE of at least 5 %. In contrast, ICIM detected five significant QTLs for DWT. Two QTLs (qDWT4.32 and qDWT5.4) were common in both methods. Among the five QTLs by ICIM, qDWT5.4 had the largest effect (PVE = 13 %) with LOD score of 7.6. All DWT additive QTLs had increasing effects coming from Pokkali alleles. Analysis of epistatic QTLs detected six pairs of interacting QTLs. All pairs of interacting QTLs except qDWT4.16 and qDWT10.19 had large effects of at least 10 % PVE. Intervals of all epistatic QTLs were independent of additive QTLs.

QTLs for Shoot-to-Root Ratio

Additive QTL mapping by IM detected three large-effect and two small-effect QTLs located on chromosomes 1 and 2. The qSRR1.382, qSRR1.36 and qSRR2.28 were highly significant and had PVE of 23, 13 and 11 %, respectively. Conversely, ICIM method identified five significant additive QTLs. Among the QTLs, two were large-effect QTLs (qSRR1.386 and qSRR2.33) with PVE of 22 and 11 %, respectively. Pokkali alleles had increasing effects in all additive QTLs for SRR. For interacting QTLs, five large-effect QTL pairs of Bengal and Pokkali origin were detected. All interacting QTLs were mapped to chromosomal regions different from additive QTLs.

Quality and Accuracy of QTL Mapping

Segregation distortion is commonly observed in populations developed from crosses between indica and japonica rice varieties. We mapped the regions of segregation distortion to determine if significant SDLs co-localized to the QTLs detected in this study. Interval mapping for SDLs detected 16 significant intervals that were skewed toward either parent (Table 6). For each chromosome, at least one SDL was mapped, except on chromosomes 2, 4, and 12. In most of the SDLs, Pokkali allele transmission was favored. In chromosome 11 alone, four significant intervals showed segregation distortion favoring inheritance of Pokkali alleles. The average interval size of SDLs was about 198Kb, with the smallest and largest interval size of 600 bp (sdl11.26) and 1.4 Mb (sdl9.12), respectively. By comparing the positions of QTLs against the positions of SDLs, the additive QTL qK1.8 and epistatic QTL qCHL9.12 overlapped exactly with sdl1.8 and sdl9.12 intervals. Therefore, these two QTLs should be considered with caution as they deviate from the expected 1:1 segregation ratio in the RIL population. The Bengal allele was transmitted to progeny lines more frequently than the Pokkali allele in sdl1.8. In contrast, Pokkali allele was favorably inherited in sdl9.12. Overall, most additive and epistatic QTLs mapped in this study were in chromosomal regions not affected by segregation distortion.
Table 6

Interval mapping of segregation distortion loci (SDLs) in Bengal/Pokkali F6 RIL population

SDL

Chromosome

Position

(cM)

Left Marker

Right Marker

Interval

LOD

Segregation ratio

 

size (bp)

Bengal

Pokkali

sdl1.8

1

63

S1_8656025

S1_8901503

245,478

7.0103

1

0.419

sdl1.12

1

74

S1_12394007

S1_12414777

20,770

6.6211

1

0.4304

sdl3.29

3

153

S3_29855008

S3_30045852

190,844

4.0197

0.5244

1

sdl3.34

3

181

S3_34487907

S3_34521908

34,001

3.4639

0.5504

1

sdl5.22

5

96

S5_22077219

S5_22142421

65,202

3.2006

0.5639

1

sdl6.4

6

23

S6_4269744

S6_4327404

57,660

3.2649

0.5605

1

sdl6.9

6

57

S6_9246940

S6_9317830

70,890

3.0751

1

0.5706

sdl7.26

7

109

S7_26680214

S7_26796826

116,612

2.5927

0.5983

1

sdl8.7

8

43

S8_7488739

S8_7668333

179,594

29.5389

0.1136

1

sdl8.16

8

52

S8_16619372

S8_16941109

321,737

22.0004

0.1761

1

sdl9.12

9

29

S9_12915373

S9_14359383

1,444,010

13.3385

0.2847

1

sdl10.12

10

31

S10_12765359

S10_12968073

202,714

2.5777

0.5992

1

sdl11.17

11

61

S11_17286328

S11_17316420

30,092

3.5648

0.5455

1

sdl11.22

11

91

S11_22242895

S11_22274274

31,379

2.8801

0.5814

1

sdl11.23

11

101

S11_23708208

S11_23866022

157,814

2.9439

0.5778

1

sdl11.26

11

115

S11_26254930

S11_26255530

600

5.3304

0.4724

1

Plant height is one of most frequently studied traits in QTL mapping. Several studies showed that plant height has high heritability and stable at different growth stages at different environments (Yan et al. 1998). In rice, 1011 QTLs were reported for plant height (gramene.org). Among these QTLs, sd1 is the main QTL that played a major role in the development of semi-dwarf varieties in rice (Khush 1999). To assess the quality of our phenotypic data and the accuracy of our QTL mapping, we surveyed plant height QTLs in rice under normal or stress conditions and compared the positions of our SHL QTLs to see if we can detect any of the previously reported plant height QTLs. In both mapping methods, the green revolution gene sd1 gene, LOC_Os01g66100 (Spielmeyer et al. 2002) was located within our major QTL designated as qSHL1.38, with LOD value as high as 36 and PVE of 51 %. The sd1 gene is about 95 Kb away from the left SNP marker and 226 Kb from the right SNP marker of qSHL1.38. Moreover, qSHL12.25 was found within the region of qPHT12-1 on chromosome 12 located between 23,603,156-26,017,884 bp region (Hemamalini et al. 2000). Also, qSHL3.34 was covered within the interval of QPh3c located between 32,945,649-36,396,286 bp of chromosome 3 (Li et al. 2003). The minor QTL qSHL1.7 was flanked within ph1.2 located in 5,941,464-7,445,919 bp region on chromosome 1 (Marri et al. 2005); while qSHL2.18 was found within the reported QTL on chromosome 2 at 17,484,665-33,939,159 bp region (Huang et al. 1996). Additionally, qSHL5.6 was confirmed within the QTL region of chromosome 5 located in between 5255, 880-6,700,408 bp region (Mei et al. 2003) and in ph5 located between 6,132,767-18,875,558 bp region on chromosome 5 (Zhuang et al. 1997). In summary, the locations of six SHL QTLs matched with previously reported plant height QTLs. In addition, four new minor QTLs were mapped in this study, each contributing at least 5 % of the plant height variation. Together with other QTLs for other traits, a total of eleven QTLs in this study were validated (Table 7). Therefore, our QTL mapping by IM and ICIM methods using ultra-high density genetic map is robust and informative.
Table 7

Summary of additive QTLs co-localizing to previously reported QTLs

Trait

QTL in this study

Previous QTL

Reference

K+ concentration

qK1.11

qSKC1

Thomson et al. (2010)

 

qK6.4

QTL on chr. 6, at 30 cM

Koyama et al. (2001)

NaK ratio

qNaK1.11

qSNK1

Thomson et al. (2010)

  

QTL on chr. 1, at 74 cM

Koyama et al. (2001)

Salt injury score

qSIS9.8

qSES9

Thomson et al. (2010)

Plant height

qSHL1.38

sd1

Spielmeyer et al. (2002)

 

qSHL1.7

ph1.2

Marri et al. (2005)

  

qPH1.2

Bimpong et al. (2013)

 

qSHL2.18

QTL on chr. 2 at 17-33 Mb

Huang et al. (1996)

 

qSHL3.34

QPh3c

Li et al. (2003)

 

qSHL5.6

ph5

Zhuang et al. (1997)

  

QTL on chr. 5 at 5.2- 6.7 Mb

Mei et al. (2003)

 

qSHL12.25

qPHT12-1

Hemamalini et al. (2000)

Shoot dry weight

qDWT6.24

qDWT6.1

Bimpong et al. (2013)

Identification of Candidate Genes Within QTLs

The saturation of SNP markers in our linkage map allowed us to detect QTLs at an interval size much shorter than previously reported QTLs. In this study, the average interval size of a QTL was 132 Kb, with minimum and maximum interval size of 22 bp and 877 Kb, respectively (Table 4). For nine traits, IM and ICIM mapped 64 and 36 additive QTLs. Fifteen QTLs were commonly detected in both methods with a total of 85 QTLs. To identify candidate genes underlying fitness of rice under salt stress, we looked at all genes in the QTL region using flanking markers. For 36 additive QTLs by ICIM, a total of 704 genes were present within QTLs (Additional file 2: Table S3), of which, 110 were annotated while the 594 genes were identified as expressed proteins, hypothetical proteins, transposon, and retrotransposon proteins. Similarly, for 64 additive QTLs identified by IM method, only 111 of 1046 genes were annotated. For the 1344 gene models in the 85 QTLs for nine traits, 79 genes were classified in 7 biological processes, 50 genes were classified into 7 molecular functions, and 49 genes were classified into 16 protein classes (Fig. 3). A large portion of the candidate genes was involved in metabolic processes and responses to stimuli. Candidate genes classified in biological regulation and localization (six transporters) were found within QTLs.
Fig. 3

Functional classification of annotated candidate genes delimited by additive QTLs for salinity tolerance. a classification by biological class; b classification by molecular function; c classification by protein class

Discussion

QTL mapping has been implemented in many breeding programs to discover genes underlying quantitative traits. However, many of these reported QTLs covered large chromosome intervals, thus, limiting the application of flanking markers in predicting the phenotype of the plant. A major constraint to previous QTL mapping studies is the number of available polymorphic markers. However, with reduction in DNA sequencing cost, high resolution QTL mapping is now possible using SNP markers. In this study, we utilized the GBS approach to develop an ultra-high-density genetic linkage map of rice for identification of QTLs for traits related to salinity tolerance. Thirty-eight SNP calls segregating in the RIL population were validated by re-sequencing the target region in both parents. Out of 38 SNP markers, only one SNP call in Bengal was not in agreement (Additional file 3: Table S2). Therefore, the GBS data have high quality SNP calls for linkage and QTL mapping. In spite of the large number of SNP markers placed on the linkage map, there were 20 gaps of about 5 cM intervals. These gaps could be due to removal of SNP markers during filtering process. Due to multiplexing of large number of DNA samples in the GBS, representation of a SNP in all samples was greatly reduced resulting in the removal of more than two-thirds of the GBS data. The linkage map closely resembled the rice genetic map of Harushima et al. (1998). Mapping of segregation distortion loci using this map indicated 16 intervals showing segregation distortion (Table 6). Two SDLs co-localized to QTLs for salinity tolerance (qK1.8 and qCHL9.12). Therefore, genetic variances contributed by these QTLs may not be accurate due to segregation distortion. In addition to availability of numerous SNP markers for linkage map construction, the quality of phenotypic estimates is equally important for QTL mapping. We assessed this by comparing our shoot length QTLs with reported plant height QTLs. Ten QTLs for SHL were detected (Table 4), of which, six QTLs for plant height including the major sd1 (qSHL1.38) co-localized to previously reported plant height QTLs. Validation of those QTLs suggests that our phenotypic and genotypic data for QTL mapping are of high quality (Table 7). With five to six markers per cM, the average QTL interval size was 132Kb. The maximum resolution of QTL was about 22 bp interval (qSIS6.20) and the largest QTL interval size was about 877Kb (qSIS11.2) (Table 4).

Previous QTL mapping studies for salinity tolerance mainly focused on detecting additive QTLs despite the complex nature of salinity tolerance. In this study, we also mapped interacting QTLs significantly contributing to the phenotypic variation of each trait under salt stress (Table 5). Di-genic interval mapping for epistatic QTLs revealed interaction of alleles from Pokkali and Bengal. In general, interacting QTLs were located in chromosome intervals independent of additive QTLs. Likewise, the variance explained by epistatic QTL pair was higher than the variance explained by individual additive QTL. For example, additive QTLs for Na+ concentration and CHL revealed very few small-effect QTLs. In contrast, many of the epistatic QTL pairs for Na+ and CHLs had larger PVE as high as 35 %. Therefore, these findings indicated the importance of epistatic QTLs in salt stress response in rice. Many of the QTLs flanked small intervals with few candidate genes. Overall, the ultra-high density genetic map and the high-quality phenotypic data facilitated a high resolution QTL mapping for salinity tolerance. In addition, the genetic map will be useful in discovery of novel QTLs for other contrasting agronomic traits between Bengal and Pokkali.

Since the beginning of the search for QTLs underlying salinity tolerance, Na+ concentration, K+ concentration, NaK ratio, and salt injury score were often investigated. Similar to previous reports, Na+ concentration was highly correlated to SIS or standard evaluation score (SES) and survival of rice plants under salt stress (Yeo et al. 1990; Platten et al. 2013). The shoot Na+ concentration also had significant positive correlation to NaK ratio and shoot K+ concentration (Table 2). The Na+ and K+ relationship implies that as shoot Na+ concentration increases, shoot K+ concentration also increases. It is likely that during salt stress, many lines do not discriminate these cations, thus, suggesting possible accumulation of Na+ and K+ in the shoot through non-selective cation channels (Demidchik and Maathuis 2007). This is evident in the high heritability of Na+ and K+ concentrations in the population (Table 1). In previous studies of QTLs for shoot Na+ concentration, QTLs were mapped on chromosomes 1 (Thomson et al. 2010), 3, 9, 11, (Wang et al. 2012), 4 (Koyama et al. 2001), and 7 (Lin et al. 2004). None of our additive QTLs for Na+ concentration co-localized to previous QTLs, but, the epistatic QTL qNa6.4 is possibly the same additive QTL in chromosome 6 at 24 cM (Koyama et al. 2001). The effects of additive QTLs for Na+ concentration were all minor. Surprisingly, four pairs of interacting intervals had significant larger effects (11–15 % PVE), suggesting that interactions among Na+ QTLs were important in the accumulation of Na+ in shoot. Alleles of Na+ QTLs from both parents contributed to shoot Na+ accumulation. In contrast, all alleles of additive QTLs for shoot K+ concentration were from Pokkali (Table 4). Therefore, it is interesting to know the underlying genes for K+ accumulation and their role in accumulation of other cations like Na+. The presence of transgressive segregants exhibiting higher concentration of shoot K+ and lower NaK ratio than Pokkali suggests the presence of positive alleles in both parents for selective cation transport during salt stress (Fig. 1). In case of Pokkali, salt tolerance response could be due to maintenance of high K+ concentration or low NaK ratio (Ren et al. 2005) and by compartmentalization of Na+ ions into the shoot vacuoles (Kader and Lindberg 2005). The strong relationship among Na+, K+, and SIS prompted us to look for the co-location of QTLs underlying these traits. Our result showed that qNa6.5 and qNaK6.5, qK1.11 and qNaK1.11, and qSIS6.2 and qNaK6.2 co-localized in the same intervals (Table 4). Therefore, it is possible that these traits shared the same underlying causal genes. The co-location of qNa6.5 and qNaK6.5 is more likely not coincidental because both alleles of the two QTLs came from Bengal and had increasing effect in the concentration of Na+ ions. On the other hand, the co-location of qK1.11 and qNaK1.11 is consistent with co-location of shoot K+ concentration, SKC1 and shoot Na+/K+ ratio, SNK1 (Thomson et al. 2010, Wang et al. 2012). Allele substitution of Bengal with Pokkali at qK1.11 had increasing effect in the shoot K+ concentration. In contrast, Bengal allele of qNaK1.11 had increasing effect on NaK ratio, thus, corroborating the desirability of Pokkali allele at the locus for salt tolerance. In previous studies, SKC1 was responsible for 10–40 % of the variation in shoot K+ concentration (Koyama et al. 2001; Lin et al. 2004; Thomson et al. 2010; Wang et al. 2012). Here, the qK1.11 accounts for only 16 % of the variation. The discrepancy in the estimation of PVE is likely attributed to differences in population size and number of markers used in different studies. The qK1.11 is covering a 52Kb interval between 11.52–11.58 Mb region on chromosome 1 with six genes. This interval is within the reported SKC1 by Thomson et al. (2010), but, downstream of 11.46 Mb region of the cloned HKT1;5 (Ren et al. 2005). While Thomson et al. (2010) assumed HKT1;5 (LOC_Os01g20160) as the underlying gene for qSKC1 or Saltol, it is also possible that other genes contributing toward salt tolerance might be present in the SKC1 region. This possibility is supported by the findings from a genome-wide association mapping study (Kumar et al. 2015), where 12 significant SNPs were located between 9.6 and 14.5 Mb region of chromosome 1. One of the 12 SNPs with high linkage disequilibrium (LD) at 11.6 Mb region (1:11608731) is 26Kb away from the right marker of qK1.11. Furthermore, HKT1;5 allele mining in several rice cultivars showed a weak association of HKT1;5 allele to low Na+ concentration to account for salinity tolerance. The HKT1;5 allele in aromatic group that included Pokkali showed low Na+ concentration. However, several cultivars having different HKT1;5 alleles (Aus, FL478, Hasawi, Daw, Japonica lines, and O. glaberrima) also showed low Na+ concentration and high salt tolerance (Platten et al. 2013). Additionally, our genetic map data showed the availability of markers that flanked HKT1;5 gene (Additional file 1: Table S1, at 70.2 cM) and the absence of segregation distortions in these regions (Table 6), but the IM and ICIM methods both detected QTL for high shoot K+ concentration downstream of HKT1;5. Interestingly, the qK1.11 interval contained two transposons, three uncharacterized expressed proteins, and a CC-NBS-LRR-encoding gene (LOC_Os01g20720). NBS-LRR genes are the largest class of resistance genes implicated in the recognition of pathogen-derived avirulence protein. In rice, a gene encoding a CC-NBS-LRR, Pb1, provided a durable panicle blast resistance by interacting with WRKY45 transcription factor for the activation of signal transduction pathway (Inoue et al. 2013). On the other hand, overexpression of ADR1 gene encoding a CC-NBS-LRR in A. thaliana showed enhanced drought tolerance (Chini et al. 2004). Therefore, the role of LOC_Os01g20720 gene in signal transduction pathway and shoot K+ ion accumulation should be investigated. Other QTLs for shoot K+ concentration such as qK1.8, qK1.38, qK5.4, qK6.4, and qK6.5 covered at least 250 kb intervals containing 33, 40, 86, 61, and 34 gene models, respectively. Candidate genes present in these QTL intervals include protein kinases, transcription factors, ethylene, auxin-responsive proteins, flavin-containing monooxygenases, and several expressed proteins of unknown function. In contrast, qNa2.7 is saturated with transposons and retrotransposons except for a putative membrane lipid channel, scramblase protein (LOC_Os02g14290). The qNa12.18 flanked four transposons and a hypothetical protein.

For NaK ratio QTLs, the co-location of qSIS6.2 and qNaK6.2 confirmed the significant correlation of NaK ratio to SIS. For both QTLs, Bengal alleles were undesirable. Only seven genes including a WRKY113 transcription factor (LOC_Os06g06360) were present in this QTL interval. Whether WRKY113 is interacting with the CC-NBS-LRR in qK1.11 or qNaK1.11 like the Pb1, presents an interesting perspective to study gene interactions and salt tolerance. In contrast, the large-effect qNaK6.5 (or qNa6.5) still covered a 264Kb interval and contained 34 gene models. Candidate genes in this interval are MYB transcription factor (LOC_Os06g10350), cyclic nucleotide-gated ion channel (LOC_Os06g10580), transcription elongation factor SPT5 (LOC_Os06g10620), and leaf senescence-related protein (LOC_Os06g10560). Among the NaK QTLs, qNaK1.11 is likely the same QTL as qSNK1 (Koyama et al. 2001; Thomson et al. 2010).

SIS reflects the overall plant’s response to salt stress. Hence, we are particularly curious in finding QTLs to identify underlying genes for this trait. Among the additive QTLs, qSIS5.1b had PVE of 13 % with increasing effect from Bengal allele. Therefore, in breeding for low SIS, the corresponding Pokkali allele at qSIS5.1b is desirable. The variance explained by qSIS6.2 alone was only 5 %, but, interaction to qSIS6.30 increased the PVE to 15 % (Table 5). This result indicated the additive and epistatic effect of a locus and emphasized the importance of QTL interactions in understanding the complexity of SIS or salt tolerance. Among previously mapped QTLs for salt evaluation score (SES) or salt tolerance rating (STR), the qSIS9.8 is located within the interval of qSES9 (Thomson et al. 2010). The qSIS2.8 interval contained 25 genes, one of which encoded a cyclic nucleotide-gated ion channel (LOC_Os02g15580). In contrast, qSIS5.1b and qSIS6.20 contained two and one gene, respectively. Both QTLs delimited a lectin protein kinase (LOC_Os06g35870, LOC_Os05g03450). In A. thaliana, lectin protein kinases were involved in the protein-protein interactions for structural stability of plasma membrane and plant cell wall (Gouget et al. 2006). Therefore, it will be interesting to see if plasma membrane stability conferred by lectin protein kinase enhances salinity tolerance. Similarly, the qSIS6.21 interval confined a single candidate gene that encodes a receptor-like protein kinase 5 precursor (LOC_Os06g36270). In qSIS5.03, a vacuolar ATP synthase (LOC_Os05g01560) is one of the four genes in the interval while a trehalose phosphatase is one of the five candidate genes in qSIS5.1a. In rice, transcript expression of a mitochondrial ATP synthase (RMtATP6) was induced in leaves by NaCl and NaHCO3 treatments and overexpression of RMtATP6 in tobacco plants showed enhanced seedling salt tolerance (Zhang et al. 2006). On the other hand, overexpression of trehalose-6-phosphate phosphatase and trehalose-6-phosphate synthase increased tolerance to drought, salt, and cold in rice (Jang et al. 2003). Also, of great interest is the qSIS6.7 interval that delimited only three genes including a pyrophosphate fructose-6-phosphate 1-phosphotransferase (LOC_Os06g13810) and a flavin monooxygenase in qSIS7.14. Pyrophosphate: fructose-6-phosphate 1-phosphotransferase was associated to seedling salt tolerance (Lim et al. 2014) while overexpression of a flavin monooxygenase designated as YUCCA enhanced drought tolerance of A. thaliana (Cha et al. 2015). Additionally, qSIS8.24, qSIS9.8, and qSIS11.2 delimited genes involved in signal transduction pathway.

Plant vigor under salt stress is a good predictor of tolerance. In addition to common traits investigated under salt stress, CHL, and growth parameters such SHL, RTL, SRR, and DWT were also examined. In soybean, salinity tolerance was determined by a major QTL for chlorophyll content (Patil et al. 2016). In contrast, additive QTLs for CHL were all minor-effect QTLs while several pairs of epistatic QTLs had PVE as high as 35 % (Tables 4 and 5). Comparison of CHL QTLs with earlier reported QTLs co-localized qCHL2.20 and qCHL3.26 within the intervals of qCHL2 and qCHL3 (Thomson et al. 2010). All other CHL QTLs are novel, thus, offering new targets for further analysis. The qCHL3.26 interval flanked a single unknown expressed protein (LOC_Os03g47190) while qCHL2.20 contained six retrotransposons and one expressed protein. Aldehyde dehydrogenase (LOC_Os02g49720) and zinc-knuckle family protein (LOC_Os02g49670) were found in qCHL2.30 interval. Arabidopsis plants overexpressing aldehyde dehydrogenase improved salinity tolerance of plants by reducing the accumulation of reactive oxygen species (Sunkar et al. 2003). Among the 44 genes in the qCHL11.1, a NAC transcription factor and a glutathione S transferase are promising candidate genes. In rice, overexpression of a NAC transcription factor showed increased tolerance to drought and salt stress (Zheng et al. 2009). Conversely, glutathione S-transferase had negative effect to drought and salt tolerance in Arabidopsis plants (Chen et al. 2012). On the other hand, qCHL11.2 interval contained seven genes, one of which encodes an HVA22. In barley and Arabidopsis, aleurone cells transformed with HVA22 inhibited the formation of GA-induced formation of vacuoles and programmed cell death (Gou and Ho 2008). Since vacuoles are important storage of Na+ for salt tolerance, HVA22 is a promising candidate gene for salt tolerance.

Among the SHL QTLs, qSHL1.38 and qSHL2.18 were validating the qPH1.2 (Bimpong et al. 2013) and qPH2 (Thomson et al. 2010), respectively, for plant height QTLs investigated under salt stress. The SHL QTLs contained many candidate genes. In addition to the major sd1 gene within qSHL1.38, other candidate genes were AP2 domain containing protein (LOC_Os01g04020) in qSHL1.1, KH domain containing protein (LOC_Os01g13100) in qSHL1.7a, auxin response factor1 in qSHL1.7b, potassium transporter (LOC_Os01g13520) in qSHL2.18, gibberellin 2-oxidase (LOC_Os05g06670) in qSHL5.3, gibberellin 3-beta-dioxygenase (LOC_Os05g08540), cytokinin-O-glucosyltransferase (LOC_Os05g08480) and auxin OsIAA15 (LOC_Os05g08570) in qSHL5.4, OsMAD66 transcription factor (LOC_Os05g11380) in qSHL5.6, and OsSAUR57 in qSHL12.25. A putative RNA-binding protein containing a KH domain was reported to be important in Arabidopsis plants for heat stress tolerance (Guan et al. 2013). In other plants, AP2/ERF transcription factors were implicated in the control of metabolism, growth, and development, and in responses to environmental stress (Licausi et al. 2013).

The relationship of Na+ concentration with SHL, RTL, DWT, and SRR were not significant. However, correlation of these traits to SIS, indicated growth inhibition with increasing sensitivity to salt stress (Table 2). For RTL, large-effect additive QTLs were detected on chromosome 3 (qRTL3.7, qRTL3.10) while the rest were minor-effect QTLs located on chromosomes 1, 2, 3, 4, 8, and 9. The majority of root length variation was explained in the epistatic QTLs. Similarly, QTLs for DWT detected only three large-effect QTLs on chromosome 5 (qDWT5.2, qDWT5.4, qDWT5.5) and all epistatic QTL pairs had PVE not lower than 10 %. In contrast, five large-effect additive QTLs were mapped on chromosomes 1 and 2 for SRR. The qSRR1.382 was located on the same interval of qSHL1.38 and so, the same sd1 gene determined the increased SRR. The fact that all DWT and SRR additive QTLs were contributed by Pokkali suggested the growth-increasing effect of Pokkali alleles under salt stress. On the other hand, the significant epistatic QTLs identified in all traits emphasized the importance of additive and epistatic effects for salinity tolerance.

The growth of roots during seedling stage under salt stress was not investigated before. All RTL QTLs in this study were new QTLs. A total of 117 genes models was delimited by 14 QTLs. In qRTL1.22, only two gene models were present, a retrotransposon and an uncharacterized expressed protein. Of particular interest is the VQ domain containing protein (LOC_Os01g46440) within qRTL1.26. In Arabidopsis, VQ-containing proteins interact with WRKY transcription factors and negatively regulate plant resistance to pathogen infection (Wang et al. 2015). Other candidate genes within RTL QTLs are aldehyde dehydrogenase (LOC_Os02g43194) and polyamine oxidase (LOC_Os02g43220) in qRTL2.26, ankyrin repeat-reach protein (LOC_Os02g54860) and trehalose-6-phosphate (LOC_Os02g54820) among seven genes contained in qRTL2.33, an integral membrane protein (LOC_Os03g11590) in qRTL3.6, MYB transcription factor (LOC_Os03g13310) and transporters (LOC_Os03g13240, LOC_Os03g13250, LOC_Os03g17740) in qRTL3.7 and qRTL3. An asparagine synthetase (LOC_Os03g18130) is within qRTL3.10, while a vacuolar protein sorting-associated protein 18 (LOC_Os08g08060), transporter (LOC_Os08g08070), and an RLK gene (LOC_Os08g08140) are delimited in qRTL8.4. The qRTL9.14 contains only three genes, one of which is a WRKY gene (LOC_Os09g25060). The qRTL8.27 contains a PDR ABC transporter gene (LOC_Os08g43120).

Koyama et al. (2001) detected one QTL for dry mass on chromosome 6 at 34 cM. A total of six DWT QTLs were mapped on chromosome 6 by IM and ICIM. However, none of our QTLs are localized at 34 cM region. The qDWT6.24, however, validated the qDWT6.1 detected by Bimpong et al. (2013). Notable candidate genes within DWT QTLs are transporters (LOC_Os01g38670, LOC_Os01g38680, LOC_Os05g04600, LOC_Os05g08430) in the intervals of qDWT1.2, qDWT5.2, and qDWT5.4, calmodulin-binding transcription factors (LOC_Os01g69910, LOC_Os05g10840) in qDWT1.40, a REX1 DNA repair gene (LOC_Os05g10980) in qDWT5.5, a MYB transcription factor (LOC_Os06g02250) in qDWT6.06, and a lectin protein kinase (LOC_Os06g35870) in qDWT6.20. In addition, a calcium-binding mitochondrial carrier (LOC_Os06g40200) is within qDWT6.23 while an ABC-type transporter gene (LOC_Os06g40550) is in qDWT6.24.

SRR QTLs under salt stress were not investigated in previous QTL mapping studies. All QTLs for SRR are new QTLs for further understanding of plant’s fitness under salinity stress. The large effect QTL qSRR1.36 spanned five genes including a WRKY119 gene (LOC_Os01g62510). The qSRR1.382 and qSRR1.386 contained an amino acid transporter (LOC_Os01g66010) and several receptor-like protein kinases. In contrast, qSRR2.31 delimited a single expressed protein. Again, a trehalose-6-phosphate (LOC_Os02g54820) and ankyrin repeat rich protein (LOC_Os02g54860) are two of the seven genes found in the qSRR2.33 interval while a HEAT repeat protein is within qSRR2.34 interval and another transporter is located in qSRR3.9. In addition to few candidate genes with known functions present within small-effect QTLs qSRR3.10, qSRR3.11, qSRR4.10, qSRR8.19, and qSRR8.26, there were several uncharacterized expressed proteins.

Taken together, at least six transporter genes were located within six QTLs, of which, three transporter genes were found in QTLs for root length (LOC_Os3g11590 in qRTL3.6; LOC_Os3g17770 in qRTL3.9, and LOC_Os3g11590 in qRTL3.7), while one transporter gene was contained in qSIS11.2 (LOC_Os11g06810), qCHL11.2 (LOC_Os11g05800), and qSHL3.34 (LOC_Os03g61290). In addition to transporters and genes for detoxification or osmotic adjustment (flavin monooxygenase, trahalose-6-phosphate), the prevalence of protein kinases suggest the role of signal transduction pathway and possible regulation of biological and cellular processes by transcription factors (Fig. 3).

Conclusion

The availability of ultra-high density genetic map and robust phenotypic data enabled us to identify additive QTLs with high resolution and facilitated identification of candidate genes. Detection of significant epistatic QTLs in addition to additive QTLs validated the complex architecture of salinity tolerance, which is possibly determined by concerted interactions of several genes. While Saltol or SKC1 may provide salinity tolerance and already being introgressed to several rice varieties in Asia, it may not provide adequate tolerance to salt stress. Our result suggested the use of multiple QTLs, especially the genes for low salt injury score to enhance salinity tolerance. The candidate genes identified in this study will be useful targets for functional genomics, gene-pyramiding, and gene-based marker-assisted breeding. Our study demonstrated the power and application of GBS for QTL mapping of a complex genetic trait like salinity tolerance.

Methods

Plant Materials and Population Development

A mapping population was developed by crossing Bengal and Pokkali as female and male parent, respectively. The resulting F1 lines were selfed and advanced by single seed descent method to generate 230 recombinant inbred lines (RILs) in F6 generation. RILs grown in unsalinized condition were extracted for DNA and were genotyped by the Cornell Genomic Diversity Facility using the GBS method.

Phenotypic Characterization and Tissue Collection

The phenotypic evaluation was conducted in the greenhouse with day time and night time temperature settings at 26–29 °C. The hydroponics system was used in the screening for seedling salt tolerance following the IRRI standard evaluation technique (Gregorio et al. 1997). The parental lines and 230 RILs were pre-germinated in a paper towel for 2 days and then transplanted to hydroponic set up containing 1 g/L of Jack’s Professional (20-20-20) (J.R. Peters, Inc.), supplemented with 300 mg/L of ferrous sulfate. The pH of the solution was maintained at 5.0–5.1 and plants were allowed to grow for 2 weeks. The whole experiment was conducted in randomized complete block design replicated three times, with ten plants per line per replicate.

At 14th day after planting, the plants were subjected to 6dSm-1 for 2 days and then into 12dSm-1 salt stress. After 6 days of salt stress, the amount of chlorophyll content was measured on the mid-length of the second youngest leaf using a SPAD-502 chlorophyll meter (Spectrum Technologies, Inc.). Five plants per line of uniform growth were evaluated for traits related to salinity tolerance. On the 9th or 11th day, when the susceptible check plants were dead, lines were phenotyped for salt injury score, shoot length, and root length. A score of 1 was given to unaffected plants, score of 3 to healthy plants but stunted, score of 5 to plants showing green leaves and stem with some tip burning and leaf rolling, score of 7 to plants with green stem but all leaves are dead, and a score of 9 to completely dead plants. Shoot length and root length were measured in centimeter. Shoot length was measured from the base of the culm to the tip of the tallest leaf. Root length was measured from the base of the culm to the tip of the longest root. Shoot length to root length ratio was derived by dividing the shoot length by the root length. For dry weight, five plants per line were collected and dried at 65 °C oven for 5 days prior to weighing.

Measurement of Na+ and K+ Concentration in Shoot

The amount of Na+ and K+ in the shoot was measured from 100 mg ground tissue taken from a pool of five plants per line. Briefly, the shoots of the plants were collected, rinsed with water, oven dried for 5 days and ground to fine powder. The tissue was digested with 5 ml of nitric acid and 3 ml of 30 % hydrogen peroxide at 152–155 °C heating block for 3 h (Jones and Case 1990). The digested tissue was diluted to a final volume of 125 ml. Flame photometer (model PFP7, Bibby Scientific Ltd, Staffordshire, UK) was used to quantify the Na+ and K+ concentrations in each sample. The final concentrations were computed from the derived standard curve of different dilutions of Na+ and K+ and the ratio of Na+ and K+ concentration (NaK) was calculated from these values.

Statistical Analyses

The phenotypic data for each trait were analyzed by ANOVA and LS mean of each line was extracted using the GLIMMIX procedure. The RIL line was entered as a fixed effect and replication as a random effect. Broad sense heritability for each trait was computed by family mean basis following Holland et al. (2003). CORR procedure was implemented to determine the relationship among traits. All data analysis was conducted using Statistical Analysis System (SAS) software version 9.4 for Windows (SAS Institute Inc 2012). Frequency distribution for each trait was constructed in Microsoft Excel 2010.

Genotyping-by-Sequencing of Bengal, Pokkali, and RIL Population

Leaf tissues were collected from each of the parental lines and RIL. The DNA was extracted using the Qiagen DNeasy Plant Mini Kit following the manufacturer’s protocol (Qiagen Inc., Valencia, CA, USA). Genomic DNA libraries were prepared as described by Elshire et al. (2011). Each DNA was cut by ApeKI enzyme and the adapters were ligated to barcode the DNA of each line. Pooled DNA from parents, 189 RILs, 94 other lines and 3 blanks was sequenced in one lane with the Illumina HiSeq sequencer at Genomic Diversity Facility, Cornell University Institute of Biotechnology (http://www.biotech.cornell.edu/brc/genomic-diversity-facility). The Tassel GBS pipeline was used to process the data and SNP calling was based on the Nipponbare reference genome MSU release 7 (Kawahara et al. 2013).

Construction of Linkage Map and QTL Analysis

Sequence alignment and SNP calling were done by the Genomic Diversity Facility, Cornell University. A total of 1,593,692 tags were sequenced, of which, 1,215,287 (76.3 %) were aligned to unique positions, 134,210 (8.4 %) had multiple alignments and 244,195 (15.3 %) were not aligned. Upon processing and filtering of SNPs, the resulting SNPs markers were reduced to a total of 33,987, with an average individual depth of 5.5 or site depth of 4.6 and individual mean missingness of 0.28. Pokkali and two RILs were declared as failed samples for having less than 10 % of the mean reads per sample. They were removed before further analysis, resulting to a total of 187 RILs for final analysis. The hapmap data file containing the filtered SNP calls were further analyzed prior to linkage map construction and QTL analysis. The Bengal parent was successfully sequenced, thus providing data for differentiation of alleles among RILs. To validate the GBS SNP calling, we amplify and re-sequenced 38 positions of GBS SNP calls in Bengal and Pokkali. Allele differentiation and allele origin among RILs were confirmed with Bengal and Pokkali re-sequenced data available in our laboratory. With the breeding scheme of the mapping population, only three possible genotypes may exist at polymorphic loci with bi-allelic SNP calling. The 2, 0, -1 coding numbers were then used to code for different alleles in the genotype data. SNP call for each marker across the population was coded as 2 if the allele was the same as Bengal. A code of 0 was given to the alternative allele and was assumed as the allele from Pokkali. Since our materials are F6 RIL, most of the loci were homozygous and should be segregating into 1:1. However, with low read depth due to highly multiplexed nature of GBS, all heterozygous SNPs (Y = T|C, M = A|C, W = T|A, R = A|G, S = C|G, K = G|T) and missing SNP (N) calls were coded as -1. All SNP markers monomorphic across the 187 RILs were removed. Likewise, all SNP markers with more than 10 % missing SNP calls were purged before further analysis. As a result, only 9303 SNP markers were retained and used for linkage and QTL mapping. The order of SNP markers along the chromosome was fixed based on the physical position of SNPs in the MSU Rice Genome Annotation (Osa1) Release 7. Genetic distances of SNP markers based on recombination rates were converted using the Kosambi mapping function. To see if segregation distortion of markers occurs in the QTLs detected in this study, interval mapping of segregation distortion locus (SDL) was also conducted. Significant SDLs were declared for loci exceeding the 2.0 LOD threshold level.

Nine traits were used for QTL mapping. The mean of three replications was used as phenotypic score for each trait. Except for salt injury score, Na+ concentration, K+ concentration, Na+/K+ ratio, chlorophyll content, shoot length, root length, shoot dry weight and shoot length to root length ratio showed normal distribution. Hence, the data were directly used for QTL mapping. For SIS, data were log transformed to improve the normality of RIL distribution prior to QTL mapping. Analysis of additive QTLs for traits related to salinity tolerance was performed by interval mapping (IM-ADD), and inclusive composite interval mapping (ICIM-ADD) methods. By interval mapping method, parameters for QTL detection were set to a scanning window size of every 1 cM with LOD threshold value set at 2.0 to declare significant QTLs. In ICIM-ADD, the parameters were set as follows: missing phenotype by mean replacement, stepwise regression method every 1 cM window size with the probability levels of entering and removing variables set at 0.001, and a second step scanning by interval mapping for significant QTL detection at LOD threshold of 2.0. Epistatic QTLs were identified by interval mapping every 5 cM window with LOD threshold set at 3.0. The phenotypic variation explained by QTLs and their genetic effect were estimated. Confidence interval of each QTL was delimited by the flanking markers within the 1-LOD drop from the estimated QTL position. QTL interval size is computed from the distance between the physical positions of left and right flanking markers. Significant QTL for each trait was named with the trait, followed by numbers indicating the chromosome location and megabase (Mb) position of the QTL. For example, qK1.8 indicates the presence of a QTL for shoot K+ concentration in chromosome 1 located at 8 Mb region. All linkage, SDL and QTL analyses were implemented in QTL IciMapping software version 4.0.6.0 (Meng et al. 2015).

Candidate Gene Prediction

To identify potential candidate genes within QTL intervals, the physical positions of SNP markers flanking the QTLs were searched in MSU Rice Genome Annotation (Osa1) Release 7. Genes contained in each QTLs were listed (Additional file 2: Table S3). To understand the roles of candidate genes in the mechanism of salinity tolerance, classification and annotation of candidate genes were inquired using the Panther Classification System (Mi et al. 2016).

Abbreviations

cM: 

centi-Morgan

GBS: 

Genotyping-by-sequencing

PVE: 

Phenotypic variance explained

QTL: 

Quantitative trait locus

RILs: 

Recombinant inbred lines

SDL: 

Segregation distortion loci

SIS: 

Salt injury score

SNP: 

Single nucleotide polymorphism

Declarations

Acknowledgments

We thank Dennis Alcalde and Anna Borjas for helping in greenhouse experiments. This research was supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture (Grant No. 2013-67013-21238) and the Louisiana Rice Research Board. This manuscript is approved for publication by the Director of Louisiana Agricultural Experiment Station, USA as manuscript number 2016-306-27533.

Authors’ contributions

PKS conceived and designed the experiment. TBD conducted the experiment, analyzed the data, and wrote the manuscript. TBD and PKS critically revised the manuscript. All authors read and approved the final manuscript.

Authors’ information

TBD: Graduate student, School of Plant, Environmental, and Soil Sciences, Louisiana State University, USA. SL: Director and Rice Breeder, Rice Research Station, Louisiana State University Agricultural Center. PKS: Professor of Plant Genetics, School of Plant, Environmental, and Soil Sciences, Louisiana State University.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
School of Plant, Environmental, and Soil Sciences, Louisiana State University Agricultural Center
(2)
Rice Research Station, Louisiana State University Agricultural Center

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